Administration of Nitric Oxide Through a Novel Copper-Chitosan Delivery System in Human Corneal and Limbal Epithelial Cell Injury
Bibliographic record
Abstract
Purpose: Nitric oxide (NO) has gained attention for its role in facilitating wound healing by promoting cell migration, while being cytoprotective in a variety of cell types. We determined the efficacy of NO, administered using a novel application of copper-chitosan treatments (Cu-Ch), in facilitating corneal epithelial wound healing using an in vitro model of corneal epithelial and limbal epithelial cell injury. Methods: Human corneal epithelial (HCE) and human limbal epithelial (HLE) cells were monitored under no-scratch (CON), untreated scratch (CS), scratch + plain chitosan composite (0%), scratch + 1% copper solution Cu-Ch (1%), and scratch + 2% copper solution Cu-Ch (2%) conditions. Cell migration, cytotoxicity, apoptosis, and total nitrate/nitrite concentrations were measured at 24, 48, and 72 hours after injury and treatment. iNOS expression in HLE cells also was determined using Western blot. Results: Wound closure significantly increased in HCE cells treated with Cu-Ch (1% and 2%) after 72 hours, while HLE cells showed a significant decrease in closure with Cu-Ch (1% and 2%) treatment compared to CS. Cytotoxic fragments decreased significantly with 1% and 2% Cu-Ch treatments in HCE cells. Nitrate/nitrite levels in HLE cells showed a significant increase with 2% Cu-Ch treatment compared to CS. This increase is complemented with an upregulation of iNOS. Conclusions: Overall, HCE wound healing was accelerated with administration of Cu-Ch treatment. Differences between HCE and HLE responses may be due to intrinsic differences in NO metabolism, as evidenced by differences in NO production, potentially caused by differences in iNOS expression with treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".